Review of relevant variables
Date: 2017-06-08
# Attach these packages so their functions don't need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
library(magrittr) # enables piping : %>%
library(dplyr)
library(TabularManifest)# Call `base::source()` on any repo file that defines functions needed below. Ideally, no real operations are performed.
source("./scripts/common-functions.R") # used in multiple reports
source("./scripts/graphing/graph-presets.R") # fonts, colors, themes
source("./scripts/graphing/graph-elemental.R") # graphs to be used in dipslays
source("./scripts/graphing/graph-complex.R") # info displays
# Verify these packages are available on the machine, but their functions need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
requireNamespace("ggplot2") # graphing
# requireNamespace("readr") # data input
requireNamespace("tidyr") # data manipulation
requireNamespace("dplyr") # Avoid attaching dplyr, b/c its function names conflict with a lot of packages (esp base, stats, and plyr).
requireNamespace("testit")# For asserting conditions meet expected patterns.
# requireNamespace("car") # For it's `recode()` function.Exposition
path_input <- "./data-unshared/derived/1-dto.rds" # product of ./manipulation/1-groom-augment.R
# path_output <- ""#Put code in here. It doesn't call a chunk in the codebehind file.# list variables to keep separated for long to wide conversion
variables_static <- c(
"id" #
,"male" # Gender
,"birth_year" # Birth year from RAND longitudinal file
,"birth_month" # Month of birth
,"race" # Race
,"hispanic" # Whether Hispanic
,"cohort" # Cohort based on birth yr
,"edu_years" # Years of Education
,"highest_degree" # Highest Degree
) # static
variables_longitudinal <- c(
"lb_wave" # Leave-behind wave
,"year" # Year
,"lb_65_wave" # Leave-behind wave at age 65 or older
,"hrs_tscore"
,"interview_date" # Interview data year and month
,"responded" #
,"proxy" #
,"hhres" #
,"countb20r" #
,"shhidpnr" #
,"rmaritalst" #
,"intage_r" #
,"rpartst" #
,"score_loneliness_3" #
,"score_loneliness_11" #
,"snspouse" #
,"snchild" #
,"snfamily" #
,"snfriends" #
,"socialnetwork_total" #
,"close_social_network" #
,"social_support_mean" #
,"social_strain_mean" #
,"social_contact_total" #
,"activity_mean" #
,"activity_sum" #
,"srmemory" #
,"srmemoryp" #
,"wrectoti" #
,"wrectotd" #
,"listassi"
,"mentalstatus_tot" #
,"vocab_total" #
,"dep_total" #
,"healthcond" #
,"exercise" #
) # not staticData
# load the product of 0-ellis-island.R, a list object containing data and metadata
dto <- readRDS(path_input)
ds_wide <- readRDS("./data-unshared/derived/lb65-data-wide.rds")# dto %>% glimpse()
class(dto)[1] "tbl_df" "tbl" "data.frame"#str(dto)# rename variables for graphing convenience, Cassandra, please move upstream when stable
ds <- dto %>%
dplyr::rename_(
"id" = "id"
, "male" = "male"
, "birth_year" = "birthyr_rand"
, "birth_month" = "birthmo_rand"
, "race" = "race_rand"
, "hispanic" = "hispanic_rand"
, "cohort" = "cohort"
, "edu_years" = "raedyrs"
, "highest_degree" = "raedegrm"
)
# subset variables of relevance for this project
ds <- ds %>%
dplyr::select_(.dots = c(variables_static, variables_longitudinal)) %>%
as.data.frame() %>%
dplyr::mutate(
male = factor(male, levels = c(1,2), labels = c("Men", "Women"))
,race = factor(race, levels = c(1, 2, 3), labels = c("White","Black","Other") )
,cohort = factor(cohort, levels = c(0, 1, 2, 3, 4, 5, 6), labels = c("Not in any cohort", "Ahead", "Coda", "Hrs", "WarBabies", "Early BabyBoomers", "Mid BabyBoomers") )
,age_at_visit = intage_r
,date_at_visit = interview_date
) %>%
tibble::as_tibble()
ds %>% glimpse(width = 105)Observations: 224,970
Variables: 47
$ id <dbl> 1010, 1010, 1010, 1010, 1010, 1010, 2010, 2010, 2010, 2010, 2010, 2010,...
$ male <fctr> Men, Men, Men, Men, Men, Men, Women, Women, Women, Women, Women, Women...
$ birth_year <dbl> 1938, 1938, 1938, 1938, 1938, 1938, 1934, 1934, 1934, 1934, 1934, 1934,...
$ birth_month <dbl> 2, 2, 2, 2, 2, 2, 10, 10, 10, 10, 10, 10, 1, 1, 1, 1, 1, 1, 9, 9, 9, 9,...
$ race <fctr> White, White, White, White, White, White, White, White, White, White, ...
$ hispanic <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ cohort <fctr> Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, ...
$ edu_years <dbl> 16, 16, 16, 16, 16, 16, 8, 8, 8, 8, 8, 8, 12, 12, 12, 12, 12, 12, 16, 1...
$ highest_degree <dbl> 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 5,...
$ lb_wave <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, 2, NA, NA, N...
$ year <fctr> 2004, 2006, 2008, 2010, 2012, 2014, 2004, 2006, 2008, 2010, 2012, 2014...
$ lb_65_wave <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, 2, NA, NA, N...
$ hrs_tscore <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.666667, 1.916667,...
$ interview_date <S3: yearmon> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2004.500, 2006....
$ responded <S3: labelled> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,...
$ proxy <S3: labelled> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0,...
$ hhres <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2, 2, 2, 2,...
$ countb20r <S3: labelled> NaN, NaN, NaN, NaN, NaN, NA, NaN, NaN, NaN, NaN, NaN, NA, 2, 2...
$ shhidpnr <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3020, 3020,...
$ rmaritalst <S3: labelled> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1,...
$ intage_r <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 68, 70, 72,...
$ rpartst <S3: labelled> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0,...
$ score_loneliness_3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 2.000000, NaN, 2.0...
$ score_loneliness_11 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.909091, N...
$ snspouse <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 1, NaN, 1, NaN, NA...
$ snchild <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 1, NaN, 1, NaN, NA...
$ snfamily <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 1, NaN, 1, NaN, NA...
$ snfriends <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 1, NaN, 1, NaN, NA...
$ socialnetwork_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, 4, NA, NA, N...
$ close_social_network <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0, 19, 0, 0, 0, 11, 0, 12, 0...
$ social_support_mean <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 11.750...
$ social_strain_mean <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 4.0000...
$ social_contact_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 28, NA, 27, NA, NA,...
$ activity_mean <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 2.65, NaN,...
$ activity_sum <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 53, NaN, N...
$ srmemory <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, 2, 3, 2, 2, NA, 2, 2...
$ srmemoryp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, 2, 2, 1, 2, NA, 2, 1...
$ wrectoti <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, 3, 5, 4, 2, NA, 6, 5...
$ wrectotd <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, 3, 7, 3, 2, NA, 6, 6...
$ listassi <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 31, 21, 11, 1, NA, 2...
$ mentalstatus_tot <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 8, 9, 9, 7, 6, NA, 9, 9...
$ vocab_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 5, NaN, 6, NaN, NA...
$ dep_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, NA, 0, 0...
$ healthcond <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, NA, 2, 2...
$ exercise <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9, 10, 12, 11, 6, NA, 1...
$ age_at_visit <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 68, 70, 72,...
$ date_at_visit <S3: yearmon> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2004.500, 2006....ds %>% names_labels() name label
1 id hhidpn: hhold id + person number /num
2 male <NA>
3 birth_year <NA>
4 birth_month <NA>
5 race <NA>
6 hispanic <NA>
7 cohort <NA>
8 edu_years <NA>
9 highest_degree <NA>
10 lb_wave <NA>
11 year <NA>
12 lb_65_wave <NA>
13 hrs_tscore <NA>
14 interview_date <NA>
15 responded inw7: =1 if respondent w7
16 proxy r7proxy:w7 whether proxy interview
17 hhres h7hhres:w7 number of people in hh
18 countb20r r7bwc20: w7 backwards count from 20
19 shhidpnr s7hhidpn:w7 spouse hhidpn
20 rmaritalst r7mstat:w7 r marital status
21 intage_r r7agey_e:w7 r age (years) at ivw endmon
22 rpartst r7mpart:w7 r partnered
23 score_loneliness_3 <NA>
24 score_loneliness_11 <NA>
25 snspouse Q7. LIVE WITH SPOUSE OR PARTNER
26 snchild Q10. HAVE ANY CHILDREN
27 snfamily Q14. HAVE ANY OTHER IMMEDIATE FAMILY
28 snfriends Q18. HAVE ANY FRIENDS
29 socialnetwork_total <NA>
30 close_social_network <NA>
31 social_support_mean <NA>
32 social_strain_mean <NA>
33 social_contact_total <NA>
34 activity_mean <NA>
35 activity_sum <NA>
36 srmemory RATE MEMORY
37 srmemoryp RATE MEMORY PAST
38 wrectoti NUMBER GOOD - IMMEDIATE
39 wrectotd NUMBER GOOD - DELAYED
40 listassi D104 WORD LIST ASSIGNMENT
41 mentalstatus_tot <NA>
42 vocab_total <NA>
43 dep_total <NA>
44 healthcond <NA>
45 exercise <NA>
46 age_at_visit r7agey_e:w7 r age (years) at ivw endmon
47 date_at_visit <NA># replace static variables with NA for those interview_dates that are NA
# this means that the person did not have any observation in that wave
# However, this is a temp fix. Address it upstream (possibly during elongation)
# these rows are the ones we've created, so we remove them now
ds %>% distinct(id) %>% count() # n = 37495# A tibble: 1 x 1
n
<int>
1 37495ds <- ds %>% dplyr::filter(!is.na(interview_date))
ds %>% distinct(id) %>% count() # n = 28225# A tibble: 1 x 1
n
<int>
1 28225# # select a single case for inspection
# ids <- sample(unique(ds$id),1)
# #What does data look like for variables that do not change with time?
ds %>% select_(.dots = variables_static) # A tibble: 117,151 x 9
id male birth_year birth_month race hispanic cohort edu_years highest_degree
<dbl> <fctr> <dbl> <dbl> <fctr> <dbl> <fctr> <dbl> <dbl>
1 3010 Men 1936 1 White 0 Hrs 12 2
2 3010 Men 1936 1 White 0 Hrs 12 2
3 3010 Men 1936 1 White 0 Hrs 12 2
4 3010 Men 1936 1 White 0 Hrs 12 2
5 3010 Men 1936 1 White 0 Hrs 12 2
6 3020 Women 1938 9 White 0 Hrs 16 5
7 3020 Women 1938 9 White 0 Hrs 16 5
8 3020 Women 1938 9 White 0 Hrs 16 5
9 3020 Women 1938 9 White 0 Hrs 16 5
10 3020 Women 1938 9 White 0 Hrs 16 5
# ... with 117,141 more rows#How many distinct values are there for each static variable?
set.seed(42)
ds %>%
select_(.dots = variables_static) %>%
# filter(id %in% sample(unique(id),100)) %>%
summarize_all(n_distinct) %>%
t() [,1]
id 28225
male 3
birth_year 91
birth_month 12
race 4
hispanic 3
cohort 7
edu_years 19
highest_degree 9#How many distinct values are there for variables that change over time?
ds %>%
select_(.dots = variables_longitudinal) %>%
summarize_all(n_distinct) %>%
t() [,1]
lb_wave 6
year 6
lb_65_wave 5
hrs_tscore 51
interview_date 87
responded 1
proxy 2
hhres 15
countb20r 5
shhidpnr 19805
rmaritalst 9
intage_r 87
rpartst 2
score_loneliness_3 10
score_loneliness_11 83
snspouse 3
snchild 3
snfamily 3
snfriends 4
socialnetwork_total 6
close_social_network 58
social_support_mean 56
social_strain_mean 241
social_contact_total 55
activity_mean 90
activity_sum 90
srmemory 7
srmemoryp 5
wrectoti 12
wrectotd 12
listassi 5
mentalstatus_tot 12
vocab_total 13
dep_total 11
healthcond 9
exercise 15Development
This section will contain a close up examination of relevant variables, one by one.
Static
This section focuses on variables with values that do not change with time.
id
# How many respondents are in the sample?
ds %>% distinct(id) %>% count()# A tibble: 1 x 1
n
<int>
1 28225ds %>% group_by(id) %>% summarize(n=n())# A tibble: 28,225 x 2
id n
<dbl> <int>
1 3010 5
2 3020 6
3 10001010 6
4 10003030 6
5 10004010 4
6 10004040 6
7 10013010 6
8 10013040 6
9 10038010 6
10 10038040 6
# ... with 28,215 more rowsmale
# what is the gender composion of the sample?
ds %>% group_by(male) %>% summarize(n=n())# A tibble: 3 x 2
male n
<fctr> <int>
1 Men 48415
2 Women 68729
3 NA 7ds %>% histogram_discrete("male")
# what is gender composition over time?
ds %>% count_over_time("year","male")
ds %>% count_over_time("lb_wave","male")Warning: Removed 3 rows containing missing values (position_stack).Warning: Removed 3 rows containing missing values (geom_text).
race
# what is the race compositon of the sample
ds %>% group_by(male) %>% summarize(n=n()) %>% neat("pandoc")| male | n |
|---|---|
| Men | 48415 |
| Women | 68729 |
| NA | 7 |
ds %>% histogram_discrete("male")
# what is race composition over time?
ds %>% count_over_time("year","race")
ds %>% count_over_time("lb_wave","race")Warning: Removed 4 rows containing missing values (position_stack).Warning: Removed 4 rows containing missing values (geom_text).
# there may not be enough sample size if split by race
ds %>%
dplyr::filter(lb_wave == 4) %>%
group_by(race) %>%
distinct(id ) %>% count()# A tibble: 3 x 2
race n
<fctr> <int>
1 White 771
2 Black 62
3 Other 35Dynamic
lb Wave
# examine hrs time
ds %>% over_time("year", "hrs_tscore")Measure : hrs_tscore
2004 2006 2008 2010 2012 2014 <NA>
0.75 . . . . 1 . .
0.833333333333485 . . . . . 4 .
0.916666666666515 . . . . 6 . .
0.916666666666742 . . . . 1 12 .
1 . . . . 18 21 .
1.08333333333326 . . . . 394 32 .
1.08333333333348 . . 4 2 172 17 .
1.16666666666652 . . . . 258 24 .
1.16666666666674 . 5 32 3 367 74 .
1.25 . 26 73 9 656 173 .
1.33333333333326 . 41 78 26 654 180 .
1.33333333333348 . 23 72 16 267 98 .
1.41666666666652 . 25 39 7 674 122 .
1.41666666666674 . 142 275 51 621 386 .
1.5 . 325 511 122 1725 781 .
1.58333333333326 . 392 407 123 1012 661 .
1.58333333333348 . 233 308 63 666 357 .
1.66666666666652 . 268 179 68 536 401 .
1.66666666666674 . 834 744 246 949 1206 .
1.75 . 1634 1414 414 1437 2261 .
1.83333333333326 . 1227 1070 304 906 1888 .
1.83333333333348 . 759 694 194 392 1029 .
1.91666666666652 . 690 493 160 500 832 .
1.91666666666674 . 1512 1629 592 791 2029 .
2 . 2185 2174 991 1346 1921 .
2.08333333333326 . 1337 1495 902 1055 638 .
2.08333333333348 . 734 408 254 439 643 .
2.16666666666652 . 596 660 476 456 205 .
2.16666666666674 . 1081 884 574 863 583 .
2.25 . 1223 1121 1077 1014 544 .
2.33333333333326 . 628 591 931 415 223 .
2.33333333333348 . 341 155 322 144 152 .
2.41666666666652 . 220 197 470 123 65 .
2.41666666666674 . 425 248 833 235 165 .
2.5 . 383 285 1371 245 147 .
2.58333333333326 . 180 113 942 102 60 .
2.58333333333348 . 82 26 456 54 37 .
2.66666666666652 . 52 20 492 24 20 .
2.66666666666674 . 104 45 782 89 30 .
2.75 . 74 40 805 80 42 .
2.83333333333326 . 19 8 379 48 2 .
2.83333333333348 . 9 . 91 12 11 .
2.91666666666652 . 1 1 117 12 . .
2.91666666666674 . 13 . 154 8 . .
3 . . . 122 10 . .
3.08333333333326 . . . 56 5 . .
3.08333333333348 . . . 6 . . .
3.16666666666652 . . . 19 1 . .
3.16666666666674 . . . 7 . . .
3.25 . . . 2 . . .
<NA> 20129 646 724 7003 771 672 . year mean sd count
1 2006 2.00 0.267 17823
2 2008 1.96 0.268 16493
3 2010 2.32 0.344 15031
4 2012 1.78 0.374 19783
5 2014 1.86 0.261 18076# examine lb wave over time
ds %>% over_time("year", "lb_wave")Measure : lb_wave
2004 2006 2008 2010 2012 2014 <NA>
1 3269 6337 5721 2587 2484 862 .
2 . 1390 1347 4663 3924 2189 .
3 . . 1 1075 1001 3620 .
4 . . . 1 1 866 .
5 . . . . . 1 .
<NA> 16860 10742 10148 13708 13144 11210 . year mean sd count
1 2004 1.00 0.000 3269
2 2006 1.18 0.384 7727
3 2008 1.19 0.393 7069
4 2010 1.82 0.638 8326
5 2012 1.80 0.656 7410
6 2014 2.60 0.837 7538# examine lb wave time
psych::describe(ds_wide$lbtime_1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 12604 0 0 0 0 0 0 0 0 NaN NaN 0table(ds_wide$lbtime_1)
0
12604 psych::describe(ds_wide$lbtime_2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 7616 3.99 0.92 4.08 4.05 0.37 1.33 10 8.67 0.83 7.57 0.01table(ds_wide$lbtime_2)
1.33333333333326 1.33333333333348 1.41666666666674 1.5 1.58333333333326 1.58333333333348 1.66666666666652
2 1 1 9 19 12 15
1.66666666666674 1.75 1.83333333333326 1.83333333333348 1.91666666666652 1.91666666666674 2
50 75 59 39 38 62 103
2.08333333333326 2.08333333333348 2.16666666666652 2.16666666666674 2.25 2.33333333333326 2.33333333333348
49 30 13 36 25 12 9
2.41666666666652 2.41666666666674 2.5 2.58333333333326 2.58333333333348 2.66666666666652 2.66666666666674
6 13 4 2 2 1 2
2.75 2.83333333333326 2.83333333333348 2.91666666666674 3 3.08333333333326 3.08333333333348
1 2 3 4 9 9 2
3.16666666666652 3.16666666666674 3.25 3.33333333333326 3.33333333333348 3.41666666666652 3.41666666666674
4 20 44 74 26 27 53
3.5 3.58333333333326 3.58333333333348 3.66666666666652 3.66666666666674 3.75 3.83333333333326
82 86 59 55 167 295 314
3.83333333333348 3.91666666666652 3.91666666666674 4 4.08333333333326 4.08333333333348 4.16666666666652
133 253 383 800 526 241 314
4.16666666666674 4.25 4.33333333333326 4.33333333333348 4.41666666666652 4.41666666666674 4.5
415 557 297 124 145 188 315
4.58333333333326 4.58333333333348 4.66666666666652 4.66666666666674 4.75 4.83333333333326 4.83333333333348
185 84 72 146 113 49 12
4.91666666666652 4.91666666666674 5 5.08333333333326 5.16666666666674 5.75 5.91666666666652
17 17 11 9 1 3 2
6 6.08333333333326 6.08333333333348 6.16666666666652 6.16666666666674 6.25 6.41666666666652
2 2 1 3 1 4 1
6.41666666666674 6.5 6.58333333333326 6.58333333333348 6.66666666666674 6.83333333333348 7.33333333333348
2 2 2 3 3 1 1
7.41666666666674 7.5 7.58333333333326 7.66666666666674 7.75 7.83333333333326 7.83333333333348
2 7 4 8 8 9 2
7.91666666666652 7.91666666666674 8 8.08333333333326 8.08333333333348 8.16666666666652 8.16666666666674
8 13 20 8 8 3 7
8.25 8.33333333333326 8.33333333333348 8.41666666666652 8.41666666666674 8.5 8.58333333333326
9 7 6 1 3 7 1
8.58333333333348 8.66666666666652 8.66666666666674 8.75 8.83333333333326 9.33333333333326 9.83333333333326
1 1 1 3 1 1 1
10
1 psych::describe(ds_wide$lbtime_3) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 2861 7.67 0.78 7.92 7.78 0.25 4 10.58 6.58 -1.09 2.08 0.01table(ds_wide$lbtime_3)
4 4.08333333333326 4.08333333333348 4.5 5.33333333333326 5.5 5.58333333333326
1 2 1 2 1 5 4
5.58333333333348 5.66666666666652 5.66666666666674 5.75 5.83333333333326 5.83333333333348 5.91666666666652
3 4 22 22 30 11 8
5.91666666666674 6 6.08333333333326 6.08333333333348 6.16666666666652 6.16666666666674 6.25
32 38 17 21 14 26 43
6.33333333333326 6.33333333333348 6.41666666666652 6.41666666666674 6.5 6.58333333333326 6.58333333333348
29 7 14 25 47 24 17
6.66666666666652 6.66666666666674 6.75 6.83333333333348 6.91666666666652 7.25 7.33333333333326
3 7 4 2 2 1 1
7.33333333333348 7.41666666666652 7.41666666666674 7.5 7.58333333333326 7.58333333333348 7.66666666666652
6 2 27 56 50 29 37
7.66666666666674 7.75 7.83333333333326 7.83333333333348 7.91666666666652 7.91666666666674 8
79 200 182 105 126 270 408
8.08333333333326 8.08333333333348 8.16666666666652 8.16666666666674 8.25 8.33333333333326 8.33333333333348
226 95 71 120 85 35 16
8.41666666666652 8.41666666666674 8.5 8.58333333333326 8.58333333333348 8.66666666666652 8.66666666666674
15 27 29 18 4 3 6
8.75 8.83333333333326 8.91666666666652 9 9.58333333333326 9.66666666666674 9.75
4 1 2 1 1 2 4
9.83333333333326 9.83333333333348 9.91666666666652 9.91666666666674 10 10.0833333333333 10.0833333333335
2 3 2 3 3 2 3
10.1666666666665 10.1666666666667 10.25 10.4166666666665 10.4166666666667 10.5833333333335
1 2 3 2 1 2 psych::describe(ds_wide$lbtime_4) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 358 9.87 0.3 9.83 9.87 0.12 7.5 10.67 3.17 -3.24 23.59 0.02table(ds_wide$lbtime_4)
7.5 7.83333333333326 7.91666666666674 8 9.41666666666652 9.5 9.58333333333326
1 1 1 1 1 4 5
9.58333333333348 9.66666666666652 9.66666666666674 9.75 9.83333333333326 9.83333333333348 9.91666666666652
9 5 29 63 52 20 26
9.91666666666674 10 10.0833333333333 10.0833333333335 10.1666666666665 10.1666666666667 10.25
40 38 2 6 12 10 11
10.3333333333333 10.3333333333335 10.4166666666667 10.5833333333333 10.5833333333335 10.6666666666667
7 3 7 1 2 1 Mental Status
# examine mental status over time
ds %>% over_time("year", "mentalstatus_tot")Measure : mentalstatus_tot
2004 2006 2008 2010 2012 2014 <NA>
0 5 8 13 4 5 9 .
1 17 22 20 18 24 26 .
2 30 48 50 46 41 58 .
3 60 61 63 75 68 76 .
4 109 90 112 131 120 110 .
5 185 181 159 265 216 172 .
6 407 365 357 752 477 450 .
7 1192 962 1006 2238 1355 1283 .
8 3260 2755 2774 5902 3183 2989 .
9 7715 5937 5884 6507 4680 4439 .
NaN 7055 7952 6699 6003 10304 9056 .
<NA> 94 88 80 93 81 80 . year mean sd count
1 2004 8.31 1.124 12980
2 2006 8.24 1.225 10429
3 2008 8.23 1.245 10438
4 2010 8.04 1.145 15938
5 2012 8.04 1.272 10169
6 2014 8.03 1.316 9612ds %>% over_time("lb_wave", "mentalstatus_tot")Measure : mentalstatus_tot
1 2 3 4 5 <NA>
0 8 . 2 . . 34
1 13 14 8 . . 92
2 24 28 13 1 . 207
3 42 38 22 3 . 298
4 88 65 29 3 . 487
5 137 143 52 7 . 839
6 409 321 153 19 . 1906
7 1272 1023 487 73 . 5181
8 3722 2619 1316 196 . 13010
9 7092 4116 2154 325 . 21475
NaN 8375 5110 1443 240 1 31900
<NA> 78 36 18 1 . 383 lb_wave mean sd count
1 1 8.29 1.068 12807
2 2 8.16 1.157 8367
3 3 8.20 1.146 4236
4 4 8.26 1.023 627set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, mentalstatus_tot
) %>%
filter(id %in% sample(unique(id),200))
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "mentalstatus_tot",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Word List Recall
# examine the assignment of word lists over time
ds %>% over_time("year", "listassi")Measure : listassi
2004 2006 2008 2010 2012 2014 <NA>
1 4642 5869 4055 4612 3844 6006 .
11 4614 5255 4071 4621 4868 4961 .
21 4533 3955 4051 4617 5743 3911 .
31 4502 3390 3900 6701 4951 2820 .
NaN 1838 . 1140 1483 1148 1050 .
<NA> . . . . . . . year mean sd count
1 2004 15.86 11.179 18291
2 2006 13.63 10.940 18469
3 2008 15.85 11.133 16077
4 2010 17.52 11.523 20551
5 2012 17.08 10.699 19406
6 2014 13.00 10.759 17698ds %>% over_time("lb_wave", "listassi")Measure : listassi
1 2 3 4 5 <NA>
1 5386 3303 1638 299 1 18401
11 5385 3273 1484 272 . 17976
21 4957 3187 1264 155 . 17247
31 4996 3431 1183 128 . 16526
NaN 536 319 128 14 . 5662
<NA> . . . . . . lb_wave mean sd count
1 1 15.61 11.183 20724
2 2 16.11 11.273 13194
3 3 14.58 11.149 5569
4 4 12.31 10.561 854
5 5 1.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, wrectoti, wrectotd, listassi
) %>%
filter(id %in% sample(unique(id),100))
# A single, elemental graph
d %>% elemental_line(
variable_name = "wrectoti",
time_metric = "age_at_visit",
color_name = "black",
line_alpha = .5,
line_size = 1,
smoothed = T
)
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "wrectoti",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "wrectotd",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Social Strain
ds %>% summarize_over_time("year", "social_strain_mean") year mean sd count
1 2006 5.35 1.658 7655
2 2008 5.24 1.671 6999
3 2010 5.29 1.652 8276
4 2012 5.24 1.699 7351
5 2014 5.20 1.670 7490ds %>% summarize_over_time("lb_wave", "social_strain_mean") lb_wave mean sd count
1 1 5.38 1.697 17815
2 2 5.21 1.659 13422
3 3 5.06 1.603 5668
4 4 5.01 1.535 865
5 5 3.31 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, social_strain_mean
) %>%
filter(id %in% sample(unique(id),100))
# A single, elemental graph
d %>% elemental_line(
variable_name = "social_strain_mean",
time_metric = "age_at_visit",
color_name = "black",
line_alpha = .5,
line_size = 1,
smoothed = T
)
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "social_strain_mean",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Loneliness Three Item Short
ds %>% summarize_over_time("year", "score_loneliness_3") year mean sd count
1 2004 1.44 0.529 3220
2 2006 1.49 0.548 7622
3 2008 1.49 0.545 6957
4 2010 1.48 0.544 8213
5 2012 1.49 0.548 7318
6 2014 1.48 0.546 7452ds %>% summarize_over_time("lb_wave", "score_loneliness_3") lb_wave mean sd count
1 1 1.50 0.553 20950
2 2 1.48 0.542 13342
3 3 1.45 0.524 5628
4 4 1.43 0.514 861
5 5 1.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, score_loneliness_3
) %>%
filter(id %in% sample(unique(id),100))
# A single, elemental graph
d %>% elemental_line(
variable_name = "score_loneliness_3",
time_metric = "age_at_visit",
color_name = "black",
line_alpha = .5,
line_size = 1,
smoothed = T
)
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "score_loneliness_3",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Loneliness Full Scale
# examine the assignment of word lists over time
ds %>% summarize_over_time("year", "score_loneliness_11") year mean sd count
1 2008 1.52 0.430 6942
2 2010 1.53 0.437 8205
3 2012 1.53 0.437 7309
4 2014 1.54 0.443 7451ds %>% summarize_over_time("lb_wave", "score_loneliness_11") lb_wave mean sd count
1 1 1.55 0.448 11463
2 2 1.52 0.431 11960
3 3 1.51 0.427 5624
4 4 1.50 0.429 859
5 5 1.55 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, score_loneliness_11
) %>%
filter(id %in% sample(unique(id),100))
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "score_loneliness_11",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Social Network
ds %>% summarize_over_time("year", "socialnetwork_total") year mean sd count
1 2004 3.38 0.811 3231
2 2006 3.40 0.754 7713
3 2008 3.32 0.800 7058
4 2010 3.33 0.784 8293
5 2012 3.18 0.859 7359
6 2014 3.14 0.882 7488ds %>% summarize_over_time("lb_wave", "socialnetwork_total") lb_wave mean sd count
1 1 3.31 0.814 21152
2 2 3.27 0.817 13459
3 3 3.21 0.851 5664
4 4 3.22 0.827 866
5 5 4.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, socialnetwork_total
) %>%
filter(id %in% sample(unique(id),100))
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "socialnetwork_total",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 7.8661e-31Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 7.8661e-31Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Close Social Network
# examine the assignment of word lists over time
ds %>% summarize_over_time("year", "close_social_network") year mean sd count
1 2004 1.62 4.574 20129
2 2006 3.74 5.960 18469
3 2008 3.70 5.967 17217
4 2010 3.33 5.663 22034
5 2012 3.17 5.569 20554
6 2014 3.48 5.701 18748ds %>% summarize_over_time("lb_wave", "close_social_network") lb_wave mean sd count
1 1 8.98 6.174 21260
2 2 8.88 6.207 13513
3 3 8.89 6.034 5697
4 4 8.85 6.071 868
5 5 5.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, close_social_network
) %>%
filter(id %in% sample(unique(id),100))
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "close_social_network",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Social Contact
# examine the assignment of word lists over time
ds %>% summarize_over_time("year", "social_contact_total") year mean sd count
1 2004 29.85 9.152 3237
2 2006 29.18 8.645 7690
3 2008 29.48 9.040 7032
4 2010 29.80 9.121 8294
5 2012 29.72 9.484 7375
6 2014 29.20 9.216 7491ds %>% summarize_over_time("lb_wave", "social_contact_total") lb_wave mean sd count
1 1 29.43 9.187 21127
2 2 29.65 9.078 13451
3 3 29.45 8.937 5674
4 4 29.65 8.824 866
5 5 36.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, social_contact_total
) %>%
filter(id %in% sample(unique(id),100))
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "social_contact_total",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Activity
# examine the assignment of activity over time
ds %>% summarize_over_time("year", "activity_sum") year mean sd count
1 2010 53.43 12.245 7341
2 2012 53.54 12.265 6064
3 2014 53.11 12.323 6511ds %>% summarize_over_time("lb_wave", "activity_sum") lb_wave mean sd count
1 1 53.78 12.662 4976
2 2 53.18 12.121 9230
3 3 53.14 12.115 4942
4 4 54.23 12.572 767
5 5 51.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, activity_sum
) %>%
filter(id %in% sample(unique(id),100))
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "activity_sum",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
srmemory
ds %>% over_time("year","srmemory")Measure : srmemory
2004 2006 2008 2010 2012 2014 <NA>
1 960 912 855 961 972 900 .
2 4381 4131 3791 4678 4734 4652 .
3 7525 7178 6871 8503 8002 7510 .
4 4231 3974 3714 5088 4588 3848 .
5 1170 991 832 1401 1093 771 .
NaN 1838 1262 1140 1385 1147 1050 .
<NA> 24 21 14 18 18 17 . year mean sd count
1 2004 3.01 0.968 18267
2 2006 3.00 0.956 17186
3 2008 2.99 0.942 16063
4 2010 3.06 0.963 20631
5 2012 3.00 0.952 19389
6 2014 2.94 0.925 17681ds %>% over_time("lb_wave", "srmemory")Measure : srmemory
1 2 3 4 5 <NA>
1 1004 643 311 41 . 3561
2 4722 3361 1481 228 . 16575
3 8547 5736 2497 390 . 28419
4 4995 2890 1113 181 1 16263
5 1348 560 167 14 . 4169
NaN 631 312 122 14 . 6743
<NA> 13 11 6 . . 82 lb_wave mean sd count
1 1 3.05 0.962 20616
2 2 2.95 0.915 13190
3 3 2.88 0.892 5569
4 4 2.88 0.851 854
5 5 4.00 NA 1
Social Support